1. DIAGNOSTIC PERFORMANCE OF A SEVEN-MARKER SERUM PROTEIN BIOSIGNATURE FOR THE DIAGNOSIS OF ACTIVE TB DISEASE IN AFRICAN PRIMARY HEALTH CARE CLINIC ATTENDEES WITH SUSPECTED PULMONARY TUBERCULOSIS
M0498
Novel N. Chegou1, Jayne S. Sutherland2, Stephanus Malherbe1, Amelia C. Crampin3, Paul L.A.M. Corstjens4, Annemieke Geluk4, Harriet Mayanja-Kizza5, Andre G. Loxton1, Gian van der Spuy1, Kim Stanley1, Leigh A. Kotzé1, Marieta van der Vyver6, Ida Rosenkrands7, Martin Kidd8, Paul D. van Helden1, Hazel M. Dockrell9, Tom H.M. Ottenhoff4, Stefan H.E. Kaufmann10, and Gerhard Walzl1 on behalf of the AE-TBC consortium
1DST/NRF Centre of Excellence for Biomedical Tuberculosis Research and SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, PO Box 241, Cape Town, 8000, South Africa. 2Medical Research Council Unit, The Gambia. 3Karonga Prevention Study, Malawi. 4Leiden University Medical Centre, The Netherlands.5Makerere University, Uganda. 6University of Namibia, Namibia. 7Statens Serum Institut, Denmark. 9Centre for Statistical Consultation, Stellenbosch University. 9London School of Hygiene and Tropical Medicine, UK. 10Max Planck Institute for Infection Biology, Berlin, Germany.
BACKGROUND
RESULTS
Utility of Serum Multi-analyte Models in the Diagnosis of TB Disease
There is an urgent need for user-friendly, rapid, inexpensive yet accurate tools for the diagnosis of tuberculosis (TB) disease at points-of-care in resource-limited settings. We investigated the accuracy of host biomarkers detected in serum samples obtained from adults suspected of having pulmonary TB disease at primary health care clinics in five African countries (Malawi, Namibia, South Africa, The Gambia, and Uganda), for the diagnosis of TB disease.
Table 2: Characteristics of TB and no-PTB cases and individuals with “Questionable TB” disease status. Abbreviations: SD, standard deviation; QFT= Quantiferon TB Gold In Tube; pos, positive; neg, negative; indet, indeterminate.
Linear discriminant analysis (LDA) models showed optimal prediction of pulmonary TB disease with seven-marker combinations. In addition to the seven analytes included in the optimal LDA biosignature, Apo-CIII, ferritin, fibrinogen, MMP-9 and TNF-α were also identified as important contributors to top models by the random forest analysis (Fig. 4).
Definite TB (n=185)
Probable TB (n=29)
ALL TB (n=214)
No-PTB (n=487)
Questionable TB
(n=6)
Age, mean±SD, yr
33.8±9.6
36.3±9.6
34.1±9.6
36.8±12.6
36.5±12.0
Male/Female
118/67
14/15
133/82
229/258
4/2
HIV pos, n(%)
47 (25)
8(28)
55(26)
114(23)
1(17)
QFT pos, n(%)
144 (78)
19(66)
164(78)
221(47)
2(33)
QFT neg, n(%)
28 (15)
10(34)
38(18)
235(49)
3(50)
QFT Indet, n(%)
8 (4)
0(0)
8(4)
19(4)
AIM
Table 5: Accuracy of the seven-marker serum protein biosignature (ApoA-1, CFH, CRP, IFN-γ, IP-10, SAA, Transthyretin) in the diagnosis of TB disease regardless of HIV infection status.
To evaluate the potential of protein serum host markers to diagnose pulmonary TB disease in primary health care clinic attendees from five African countries.
Training set (n=491) 
Sensitivity
Specificity
PPV
NPV
%, (n/N)
95% CI
86.7 (130/150)
( )
85.3 (291/341)
( )
72.2
( )
93.6
( )
 Test set (n=210)
81.3(52/64)
( )
79.5(116/146)
( )
63.4
( )
90.6
( )
Accuracy of the biosignature after selection of cut-off values optimized for sensitivity
Training set (n=491)
90.7 (136/150)
( )
74.8 (255/341)
( )
61.3
( )
94.8
( )
93.8 (60/64)
( )
73.3 (107/146)
( )
60.6
( )
96.4
( )
MATERIALS AND METHODS
We prospectively collected serum samples from individuals presenting with symptoms warranting investigation for pulmonary TB at the respective clinics, prior to assessment for TB disease. Using the Luminex multiplex platform, we evaluated 22 host protein biomarkers including IL-1ra, TGF-α, IFN-γ, IP-10, TNF-α, IFN-α2, VEGF, MMP-2, MMP-9, ApoA-1, Apo-CIII, transthyretin and complement factor H (kits purchased from Merck Millipore, Billerica, MA, USA), and CRP, SAA, SAP, fibrinogen, ferritin, TPA, PCT, haptoglobulin and alpha-2-macroglobulin (A2M) (kits from Bio-Rad Laboratories, Hercules, CA, USA). On the basis of laboratory, clinical and radiological findings and a pre-established diagnostic algorithm (Table 1), participants were classified into the following groups: definite TB, probable TB, questionable TB disease status or non-pulmonary TB. Participants were randomly assigned to training and test sets and biosignatures identified on the training sample set validated on the test set using multi-marker modelling approaches (Linear Discriminant Analysis or random forest).
Utility of Individual Serum Biomarkers in the Diagnosis of TB Disease
The AUCs were between 0.70 and 0.84 for 10 analytes: CRP, ferritin, fibrinogen, IFN-γ, IP-10, TGF-α, TPA, transthyretin, SAA and VEGF (Fig. 2). Sensitivity and specificity were both >70% for six of these analytes, namely; CRP, ferritin, IFN-γ, IP-10, transthyretin and SAA
Table 1: Harmonized definitions used in classifying study participants
Classification
Definition
Definite TB
Sputum culture positive for MTB OR 2 positive smears and symptoms responding to TB treatment OR
1 Positive smear plus CXR suggestive of PTB
Probable TB
1 positive smear and symptoms responding to TB treatment OR
CXR evidence and symptoms responding to TB treatment
Questionable
Positive smear(s), but no other supporting evidence
OR CXR suggestive of PTB, but no other supporting evidence OR Treatment initiated by healthcare providers on clinical suspicion only. No other supporting evidence
No-PTB
Negative cultures, negative smears, negative CXR and treatment never initiated by healthcare providers
Figure 2: Levels of host markers detected in serum samples from pulmonary TB cases (n=214) and individuals without TB disease (n=487) and ROC curves showing the accuracies of these markers in the diagnosis of TB disease, regardless of HIV infection status. Representative plots for CRP, SAA, IP-10, ferritin, IFN-γ and transthyretin are shown. Error bars in the scatter-dot plots indicate the median and Inter-quartile ranges.
Abbreviations: CXR, chest X ray; MTB, Mycobacterium tuberculosis; TB, pulmonary tuberculosis, No-PTB, non-“pulmonary tuberculosis”.
Excluded patients (n=9)
Pregnant (n=1)
Data capture issues (n=8)
Eligible patients
(n=716)
Clinical and laboratory assessment/
Reference standard
Host markers evaluated
(n=707)
Probable TB
(n=29)
Definite TB
(n=185)
No-PTB
(n=487)
Questionable
TB (n=6)
Excluded from final analysis
(n=6)
Completion of CRF
Collection of samples
Training set
(n=491; 168 TB, 323 No-PTB) TB
(n=214)
Data Analysis
ROC curve analysis
Random allocation into a training set (70%) and test set (30%)
Test set
(n=210; 77 TB, 133 No-PTB)
Different Host Markers are affected Differently by HIV Infection
Figure 4: Inclusion of different analytes into host biosignatures for the diagnosis of TB disease. (A) Frequency of analytes in the top 20 most accurate LDA seven-marker biosignatures for diagnosis of TB disease regardless of HIV infection status. (B) Importance of analytes in diagnostic biosignatures for pulmonary TB disease as revealed by random forest analysis. (C) ROC curve showing the accuracy of the finally selected seven-marker biosignature in the diagnosis of pulmonary TB disease irrespective of HIV status. (D) Frequency of analytes in the top 20 LDA biosignatures for diagnosis of TB disease in HIV-uninfected individuals. The ROC curve for TB vs no-PTB, regardless of HIV (C) was generated from the training dataset. A
DISCUSSION
New rapid, field-friendly TB diagnostic tests will be highly beneficial if based on easily obtainable samples which can immediately be used ex vivo
Such tests will yield faster results if rapid detection platforms are employed e.g. lateral flow technology
Single host markers have limited accuracies for TB due to poor specificity
Non-specificity of markers can be overcome by combining multiple classes of biomarkers, produced by different cell types
Markers that perform relatively well in HIV-infected individuals(e.g. CRP and SAA) help in identifying patients who are missed by markers that may be more often affected by HIV infection (e.g. IFN-γ and IP-10).
A test with high negative predictive value would identify patients who require confirmatory testing with centralized, technically more demanding tests (e.g. culture and GeneXpert)
Figure 1: STARD diagram showing the study design and classification of study participants. 716 individuals were prospectively evaluated in the study. 185 (26.2%) of the study participants were definite TB cases, 29 (4.1%) were probable TB cases, representing the active TB group (214 participants; 30.3%), whereas 487 (68.9%) were No-PTB cases and 6 (0.8%) had an uncertain diagnosis (questionable) and 9 were excluded.
CRF, case report form; TB, Pulmonary tuberculosis; No-PTB, Individuals presenting with symptoms and investigated for pulmonary TB but in whom TB disease was ruled out; ROC, Receiver operator characteristics. B
CONCLUSION
We have identified a promising seven-marker serum host protein biosignature for the diagnosis of active pulmonary TB disease in adults regardless of HIV infection status or ethnicity.
These results (94% sensitivity, 96% negative predictive value) hold promise for further development into a field-friendly point-of-care screening test for TB.
ACKNOWLEDGMENTS
We are grateful to all our study participants, and support staff at the different laboratories
This work was supported by the EDCTP, grant number IP_2009_32040, via the African European Tuberculosis Consortium (AE-TBC, with Prof. Gerhard Walzl as Principal Investigator.
Figure 3: Areas under the ROC curve for individual analytes. AUCs obtained after data from pulmonary TB and no-PTB patients were analysed after stratification according to HIV infection status is shown as histograms (A) or ‘Before and after’ graphs (B).